Partially Adaptive Estimation of the Censored Regression Model

Data censoring causes ordinary least squares estimates of linear models to be biased and inconsistent. Tobit, semiparametric, and partially adaptive estimators have been considered as possible solutions. This paper proposes several new partially adaptive estimators that cover a wide range of distributional characteristics. A simulation study is used to investigate the estimators’ relative efficiency in these settings. The partially adaptive censored regression estimators have little efficiency loss for censored normal errors and may outperform Tobit and semiparametric estimators considered for non-normal distributions. An empirical example of out-of-pocket expenditures for a health insurance plan provides an example, which supports these results.